OpenSEM Forums
Benny
Joined: 05/15/2020
Categorical Outcome GLM-Approach
Hello,
besides the approach where a binary or ordinal outcome is modelled via a threshold model: is it possible to model a regression with a binary outcome using a probit or logit link function like in the Generalized Linear Model (GLM) framework? If so: Could you give me a hint how to implement it?
Thanks!
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kumarsonu928292
Joined: 10/22/2020
What Things Should I Carry When Migrating from India to Australia?
hello, helpies.. i am migrating to Australia and want to know some important things. "What Things Should I Carry When Migrating to Australia" anyone with the same experience or any people from Australia who knows better. TIA..
Hilton28
Joined: 07/16/2020
Univariate ACE model for a binary variable
Hello,
Does anyone have an OpenMx script for running a Univariate ACE model for a binary variable? I would like to determine the proportion of variance accounted for by additive genetic, shared, and unique environment for a binary variable that indicates whether someone is or is not a member of a certain class of psychopathology.
Thank you in advance!
Emily
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jpritsker
Joined: 09/27/2020
Nesting with movement between clusters over time
I'm considering a model where children are nested within schools, but may change schools over time. So, I have data structured as CHILDID, Y1, X1, SCHOOLID1, Y2, X2, SCHOOLID2, ...
I initially considered having a single random effect value per school, and having $y_{i,t,school} = \mu_{school} + \dots$, but I'm not sure how to index the same random effect by different variables over time (i.e. mu[SCHOOL1], mu[SCHOOL2]) in this way.
benruk
Joined: 12/09/2019
RAM Cholesky Decomposition
Hello,
I am playing around with different kinds of twin models which I try to implement using the RAM-matrix approach, but not defining the expectation object with mxExpectationRAM(), but with mxExpectationNormal(). My idea is to create the A, S (one for MZs and one for DZs) and F matrices as well as the Identity matrix with the mxMatrix() functions and then creating the expected covariance matrices for MZs and DZs with the mxAlgebra() function following the RAM matrix formula.
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rachelg
Joined: 09/22/2020
Item parcels
I was wondering whether anyone could help me with a query about using item parcels versus summary suscale scores in a CFA model. I have a model that has 5 factors each with 2 indicators that are subscale scores of different measures (3 measures in total). These subscales have been validated in the sample that I'm using.
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pehkawn
Joined: 05/24/2020
Latent variable interactions/moderation
I am currently trying to create a latent interaction regression model in OpenMx where I have four latent variables (C, H, T, S) that predicts a fifth latent variable (A). In addition, interaction effects between the main latent predictor C, and H, T, and S should be included.
[![Latent interaction regression model][1]][1]
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Chen SJ
Joined: 04/02/2020
the result of bivariate moderation model not the same
Hello everyone!
I am trying running a bivariate moderation model put forward by Purcell with binary moderator(smoke or not) and binary outcome(T2DM or not). I have running the model twice with the same syntax and the same data without any change, However, the results are different, not only the point estimate but also the -2LL . My syntax and results are in the supplementary materials.
Arthur
Joined: 02/13/2020
Genetic correlation bellow -1
Dear OpenMx team and users,
I am running an AE multivariate twin model with a sample of 21 DZ twin pairs and 50 MZ twin pairs on 4 variables. The Genetic correlation between two of these variables is -1.10.
So my obvious question is; what possible reasons could explain this correlation bellow -1? Could it be related to the absence of "C" or "D" in the model? Or could it be related to the sample size?
Many thanks,
Arthur
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twolf
Joined: 04/24/2020
Large multivariate ACE-model with less parameters?
Hi!
I am currently interested in understanding how well a large amount of variables (20-40) which do not represent a construct in the psychometric sense but can be broadly viewed as a certain class of influences explains an outcome in comparison to another set of variables. On the phenotypical level this is quite straight forward - I can fit regression models and look at (incremental) R2-values or train a LASSO or something similar on a training set to compare the predicted R2's on the validation set.